Data extraction using AI refers to the automatic identification and extraction of relevant information from unstructured or semi-structured data sources, such as text documents or images. 

One example of using AI for data extraction is the combination of Natural Language Processing (NLP) and Computer Vision (CV) to extract data from invoices. NLP identifies the relevant text fields such as vendor name, invoice number, and date, while CV locates the exact location of these fields on the invoice image. This automated approach saves time and effort compared to manual data entry, and the extracted data is stored in a structured format for further analysis or processing.

Data extraction currently presents as a more effective alternative to Optical Character Recognition (OCR). Optical Character Recognition is a technology used to convert scanned images of text into machine-readable text. While OCR can be useful for extracting text from documents, it has some limitations when it comes to extracting structured data from unstructured or semi-structured sources such as invoices.

This is why the combination of NLP and CV for data extraction offers several advantages over OCR. First, NLP and CV can work together to extract specific pieces of information from a document, such as a vendor name, invoice number, and date, rather than just extracting all the text. This means that the extracted data can be more accurate and relevant, saving time and effort during the validation process.